Emotion-Aware Ensemble Learning (EAEL): Revolutionizing Mental Health Diagnosis of Corporate Professionals via Intelligent Integration of Multi-Modal Data Sources and Ensemble Techniques

Abstract

In this contemporary landscape of corporate environments, the increasing prevalence of mental health challenges necessitates the development of innovative diagnostic methodologies. This research introduces the Emotion-Aware Ensemble Learning (EAEL) framework, a cutting-edge approach designed to revolutionize early mental health diagnosis among corporate professionals. EAEL integrates machine learning and deep learning paradigms to process multimodal data, including facial expression analysis and typing pattern recognition, offering a holistic evaluation of emotional well-being. Our investigation methodically trains base classifiers, such as Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forests (RF), on distinct and combined datasets derived from facial expressions and typing patterns. The EAEL framework demonstrates robust performance, achieving an accuracy of 0.95, precision of 0.96, recall of 0.94, and F1-Score of 0.95 when applied to the integrated dataset. These findings underscore EAEL’s transformative potential as a proactive tool for mental health interventions in corporate settings. Future iterations could enhance the framework by incorporating physiological signals, such as heart rate variability and EEG data, further improving diagnostic accuracy. EAEL’s ability to seamlessly integrate diverse data modalities not only sets a new standard for technology-driven mental health assessments but also promises substantial benefits for employee welfare and organizational effectiveness, with the potential for adaptation in clinical environments as well.

Keywords

• Mental health diagnosis
• machine learning
• ensemble learning
• deep learning
• corporate well-being
• predictive analytics